Modelling, Simulation and Optimization of Refining Processes

Jacques Niederberger, M.Sc.PETROBRAS Research & Development Center (CENPES)

August/2009

Summary

Introduction Oil characterization Modelling Refining Processes Optimization Aspects

Introduction: PETROBRAS operations and R&D

Proved Reserves :15.1 billion barrels of oil and gas equivalent (boe)

15 Refineries Installed Capacity: 2.125 million bpd

Gas stations: 6,485

Net Operating RevenuesUS$ 127 billion (2008)

Total Investments:US$ 29 billion in 2008

Natural Gas Production: 420 thousand boe per day

Oil Production:1,980 thousands barrels per

day (bpd) of oil and LPG

Dec 2004

Thermoeletric EnergyPlants : 10Installed Capacity : 1,912 MW

PETROBRAS

AN INTEGRATED ENERGY COMPANY

Natural Gas Sales:65 million m3/d

Employees: 74,204

PETROBRAS

INDUSTRIAL UNITS IN BRAZIL

R&D EXPENDITURES

0250500750

1.0001.2501.5001.7502.000

2000 2001 2002 2003 2004 2005 2006 2007 2008

Ano

R$

MM

EXAMPLES OF MAIN CHALLENGES Ultra deep water production technology

Production in the Pre-salt sequence

Lower environmental impact products

Better output products

Zero discharge / zero emissions processes

Optimization &

Reliability

14 TECHNOLOGY PROGRAMS

Pre-salt

CENPES 137 Laboratories

TECHNOLOGICAL INTEGRATION

R&D CENTER

Types:

Contracts and agreements with Universities and Research Centers

National networks of excellence - about different oil & gas themes

Over 120 Brazilian Institutions

Types: Joint Industry Projects Cooperating Research Strategic Alliances Technology Interchange

Over 70 International Institutions

Oil Characterization

• What is oil ?

• Where does it come from ?

Complete assay contains: Distillation curve Specific Gravity curve Light end contents Viscosity Sulphur, nitrogen and metals contents Other properties

EXPERIMENTAL DATA

TRADITIONAL CHARACTERIZATION

PROCEDURE

True Boiling Point Curve - TBP

• Product withdraws at constant volume or at constant temperature

• Near ideal fractionation

• Long time demanded, high cost

Crude Oil TBP

% vaporized

tem

pera

ture

, o C

TRADITIONAL CHARACTERIZATION

PROCEDURE

Crude Oil TBP

% vaporized

tem

pera

ture

, o C

TRADITIONAL CHARACTERIZATION

PROCEDURE

Distillation curve, Specific gravity Pseudo-components

Characterization Method

Pseudo-component: fake component, oil fraction.

Crude oil and its derivatives are hydrocarbons mixtures, well described by cubic equations of state (SRK, PR)

The characterization method provides pseudo-component properties: Tc, Pc, w, PM, d60, Teb, etc.

TRADITIONAL CHARACTERIZATION

PROCEDURE

IMPROVED CHARACTERIZATION

Instead of pseudocomponents, real molecules.

• Group of molecules typically present in a determined fraction

• Bulk properties: distillation curve and specific gravity

• Mixture composition obtained through an optimization method

Modelling Refining Processes

TYPICAL REFINERY SCHEME

Processes involving chemical reactions:

Heavy Feedstock → Gases + LightDistillates + Medium Distillates +unconverted

or Heavy Feedstock + H2 → Organic Gases

+ H2S + NH3 + Light Distillates + MediumDistillates + unconverted

EFFECTS OF THE CHARACTERIZATION

METHOD

How to model chemical reactions ?

Kinetics x Thermodynamics

Kinetics: reaction order, kinetic parameters

Thermodynamics: Gibbs free energy

EFFECTS OF THE CHARACTERIZATION

METHOD

Either Kinetics or Thermodynamics require pure component data.

EFFECTS OF THE CHARACTERIZATION

METHOD

Pseudo-component approach: not good!

Compositional approach: no big deal!

If we characterize using molecules:

EFFECTS OF THE CHARACTERIZATION

METHOD

•How to build phenomenological models of conversion processes dealing with pseudocomponents ?

•Relating the overall conversion and product profile to bulk properties of the feedstock and process conditions.

EFFECTS OF THE CHARACTERIZATION

METHOD

•We model phase equilibrium and separation process with the traditional tools provided by Thermodynamics

REFINING PROCESSES MODELLING

•And for the conversion processes we build semi-empirical models

REFINING PROCESSES MODELLING

•Main conversion processes:FCC – fluid catalytic crackingDelayed CokingHydrotreatingHCC – catalytic hydrocracking

For instance, in the FCC process:

Gasoil → Combustible gas + LPG + Naphta + LCO + DO + coke

•Overall conversion depends on:feedstock propertiescatalyst propertieshardware geometry process conditions

REFINING PROCESSES MODELLING

•Product profile depends on:feedstock propertiescatalyst propertieshardware geometry process conditions

•Product properties depend on:...

REFINING PROCESSES MODELLING

How do we address any other effect not directly taken into account by the semi-empirical model ?

REFINING PROCESSES MODELLING

Introducing adjustable tuning parameters in the model.

Process data is necessary for fitting the parameters.

Quality of the model predictions equals the quality of process and feedstock data

REFINING PROCESSES MODELLING

Optimization Aspects

What does optimization mens ?

REFINING PROCESSES OPTIMIZATION

Generally speaking, any improvement in a process with a few degrees of freedom may be called optimization.

From our point of view, optimization is finding THE best solution, in a system with one ore more degrees of freedom.

SCOPE X TIME SCALE

The scope of the optimization problem and the time horizon varies in the same direction.

Task Scope Time horizon

Planning operations andinvesments for the nextyears

All the eleven Petrobras’refineries

5 to 20 years

Designing a new plant One or more units of arefinery

5 years

Planning the productionof a sigle industrial plant

One single refinery Monthly, weekly

Optimizing operatingconditions of one ormore units of a singleplant

Crude distillation + FCCconverter + FCCfractionation section of arefinery

Every 1 or 2 hours

SCOPE X MODEL COMPLEXITY

The larger the scope, the simpler must be the model.

Task Model typePlanning operations and investments for thenext years

Linear models (linear programming)

Planning the production of an entire refinery Linear models (linear programming)

Designing a new unit Rigorous mixed integer-non-linear models (MINLP)

Optimizing operating conditions of one ormore units of a single plant

Rigorous non-linear models

OPTIMIZATION & PROCESS DESIGN

Design

Analysis

Final Design

Optimization

Mass & energy balances

Equipment sizing and cost estimates

EconomicEvaluation

Parametric Optimization

StructuralOptimization

Synthesis

Decision variables

Initial estimates

OPERATING CONDITIONS OPTIMIZATION - OFF LINE

PROCESS DATA

UNIT

CONTROL SYSTEM

MODEL TUNING & OPTIMIZATION

DATA RECONCILIATION RECONCILED PROCESS DATA

PROCESS ENGINEER

OPERATOR

GROSS ERRORS DETECTION

MAINTENANCE

PROCESS AUTOMATION HIERARCHY

OPERATING CONDITIONS OPTIMIZATION - RTO

Many plants don’t have a much stable operation.

Optimal conditions for one determined run may not be the best for another run.

If optimization is off-line, we need to re-optimize for every different run.

OPERATING CONDITIONS OPTIMIZATION - RTO

Imagine if we had an optimization machine that could read process data at real time, tune automatically the process model, run automatically the optimization problem and send automatically the optimal conditions for the digital control system …

That would be Real Time Optimization -RTO.

RTO STRUCTURE

Hibernation

Steady State Detection

Model tuning

Optimization

Solution obtained?

New setpoints for the control system

Yes

No

Stationary ?

Yes

No

Real Time Optimization

RTO benefits

PETROBRAS experience: RTO implemented onDistillation and FCC Units using Equation Oriented andSequential Modular approaches

Real Time Optimization FCC Example: Operational modifications (Reaction

temperature, Feed temperature and Main Fractionatortop reflux) due to RTO

RTO benefits

RTO runs only when the unit is Steady but what is Steady State? commercial applications use a kind of statistical

approach (mean, std dev, Student and F-test) alongwith some heuristics (“tuning factor”) on a set of themost representative variables (temperatures and flowrates linked to the unit heat and mass balance)

do we really have to wait Steady-State? it can take 1-2 hours between runs if a disturbance enters the unit in between no RTO

run maybe for a long period Change the “tuning factor” or improve APC / Regulatory

control

RTO Challenges

Real Time Optimization How to deal with the “unknown” feed composition (especially

in Distillation)? Online analyzers NMR or NIR? Lab analysis frequence? Methods? Feed Reconciliation as long as you have

confidence on the model, use it as an analyzer Redistribute the amount of the pseudocomponents in

order to match some information from the unit(operations and product quality) It is an optimization problem maybe the most difficult

one (more than the profit optimization)

RTO Challenges

Real Time Optimization Non convergence tracking: it is a hard task, sometimes, to

find out the origin of the failure, especially, when it is notassociated with instrumentations or well-known processproblems

Initialization techniques

Scaling: heuristic rules X numerical analysis of the system

Integrating multiple process unities: how to deal with theincreasing problem size to get the most of integrated unitiesoptimization and its flexibilities?

How to deal with non convergence?

RTO Challenges

Real Time Optimization

Entire plant rigorous RTO – feasible, but still not possible

Multi-scale Optimization: integration and informationexchange between different optimization levels is an issuethat demands more attention

Dynamic RTO: it is still an open issue Computational efforts? Numerical issues? How to implement it on industrial applications?

RTO Challenges

Questions ?